AN IMPROVED ELECTROPHORETIC METHOD FOR THE DETERMINATION OF SERUM MILK PROTEIN VARIANTS IN GYR-HOLSTEIN COWS

Milk serum proteins such as alpha-lactalbumin (ALA) and beta-lactoglobulin (BLG) present biochemical polymorphism which is under the control of codominant autosomal alleles. In the present report, we propose modifications of traditional electrophoretic techniques such as increasing the running gel concentration from 5 to 10% and the addition of 5 M urea to the stacking gel, which permitted the detection of two variants (A and B) at the ALA and BLG loci. About 8 mul of milk serum (6 mg/ml protein) and 10 pl of total fresh milk were applied. Bovine serum albumin (BSA) and immunolactoglobulins (ILG) could also be discriminated. Total fresh milk was as useful as the purified serum milk proteins for the discrimination of ALA and BLG serum milk protein polymorphism by alkaline vertical slab polyacrylamide gel electrophoresis. However, BSA and ILG ran with caseins, which prevented their characterization in this system.

Analysis of organochlorine pesticides in plain milk using direct injection on an ISRP column, with column switching

A simple and rapid procedure for extraction and separation from aldrin, DDT, endrin, heptachloro- and methoxychloro-organochlorine pesticides in raw milk has been developed by direct injection into an HPLC system without pretreatment of the samples, using an ISRP column.

Isolation of Candida spp. from mastitic bovine milk in Brazil

The purpose of this study was to isolate yeast (Candida) from the quarter milk of cow udders from 37 dairy farms in Brazil and to identify the different species involved in mastitis. The samples were collected between October 2002 and February 2003. Two-hundred-and-sixty milk samples from cows with clinical and subclinical mastitis were examined. Milk samples were plated onto Blood agar, Mac Conkey agar and Sabouraud dextrose agar. Forty-five (17.3%) samples were positive for the genus Candida. The Candida species isolated were C. krusei (44.5%), C. rugosa (24.5%), C. albicans (8.9%), C. guilliermondii (8.9%), and others (13.2%). We also isolated Escherichia coli (26.5%), coagulase-positive Staphylococcus (25.0%), Streptococcus spp. (8.1%), Enterobacter spp. (8.1%), and other fungi (8.1%), among others.

On-line extraction and determination of carbofuran in raw milk by direct HPLC injection on an ISRP column

A method has been developed for extraction and determination of carbofuran in milk. The method involved direct injection of raw milk on to a human serum albumin dimethyloctyl-silica gel (HSA-Cs) column and the use of 80:20 (v/v) 0.01 M phosphate buffer pH 5.5 - acetonitrile as mobile phase. UV spectrophotometric detection was performed at 220 nm. Identification was based on retention time. Quantification was performed by automatic peak-area determination and was calibrated by use of an external standard.

Genetic parameters for milk yield, age at first calving and interval between first and second calving in milk buffaloes

Genetic parameters for the relation between the traits of milk yield (MY), age at first calving (AFC) and interval between first and second calving (IBFSC) were estimated in milk buffaloes of the Murrah breed. In the study, data of 1578 buffaloes at first lactation, with calvings from 1974 to 2006 were analyzed. The MTDFREML system was used in the analyses with models for the MY, IBFSC traits which included the fixed effects of herd-year-season of calving, linear and quadratic terms of calving age as covariate and the random animal effects and error. The model for AFC consisted of the herd-year-season fixed effects of calving and the random effects of animal and error. Heritability estimates MY, AFC and IBFSC traits were 0.20, 0.07 and 0.14, respectively. Genetic and phenotypic correlations between the traits were: MY and AFC = -0.12 and -0.15, MY and IBFSC = 0.07 and 0.30, AFC and IBFSC = 0.35 and 0.37, respectively. Genetic correlation between MY and AFC traits showed desirable negative association, suggesting that the daughters of the bulls with high breeding value for MY could be physiological maturity to a precocious age. Genetic correlation between MY and IBFSC showed that the selection of the animals that increased milk yield is also those that tend to intervals of bigger calving.

Buffalos milk yield analysis using random regression models

Data comprising 1,719 milk yield records from 357 females (predominantly Murrah breed), daughters of 110 sires, with births from 1974 to 2004, obtained from the Programa de Melhoramento Genetic de Bubalinos (PROMEBUL) and from records of EMBRAPA Amazonia Oriental - EAO herd, located in Belem, Para, Brazil, were used to compare random regression models for estimating variance components and predicting breeding values of the sires. The data were analyzed by different models using the Legendre's polynomial functions from second to fourth orders. The random regression models included the effects of herd-year, month of parity date of the control; regression coefficients for age of females (in order to describe the fixed part of the lactation curve) and random regression coefficients related to the direct genetic and permanent environment effects. The comparisons among the models were based on the Akaike Infromation Criterion. The random effects regression model using third order Legendre's polynomials with four classes of the environmental effect were the one that best described the additive genetic variation in milk yield. The heritability estimates varied from 0.08 to 0.40. The genetic correlation between milk yields in younger ages was close to the unit, but in older ages it was low.

Modeling thermal conductivity, specific heat, and density of milk: A neural network approach

The accurate determination of thermophysical properties of milk is very important for design, simulation, optimization, and control of food processing such as evaporation, heat exchanging, spray drying, and so forth. Generally, polynomial methods are used for prediction of these properties based on empirical correlation to experimental data. Artificial neural networks are better Suited for processing noisy and extensive knowledge indexing. This article proposed the application of neural networks for prediction of specific heat, thermal conductivity, and density of milk with temperature ranged from 2.0 to 71.0degreesC, 72.0 to 92.0% of water content (w/w), and 1.350 to 7.822% of fat content (w/w). Artificial neural networks presented a better prediction capability of specific heat, thermal conductivity, and density of milk than polynomial modeling. It showed a reasonable alternative to empirical modeling for thermophysical properties of foods.

Genetic and environmental effects over milk production of buffalo cows in Brazil

The objective of this paper was to evaluate the relevance of environmental and genetics effects on milk production of buffalo cows in Brazil. The data were based on the Buffalo Genetic Improvement Program - PROMEBUL, using information of 1,911 cows (107 Jafarabadi, 101 Mediterranean, 1,056 Mu/Tab and 647 crossbred females) with parturition between 1982 and 2003. The mathematic model for evaluating milk production included the fixed effects of herd, parturition year (1982 to 2003) and month (January to December), calf's sex (male or female), genetic group (Jafarabadi, Mediterranean, Murrah, and crossbreed), number of milking (one or two), lactation order (1 to 12) and duration of lactation (as a linear effect). The mean milk production in herds was 1,590.36 +/- 609.25 kg. All sources of variation were significant (P<0.05) for the studied characteristics, except calf's sex. The mean milk production per genetic group was 1,651.4; 1,592.2; 1,578.3 and 1,135.5 kg, for Murrah, Mediterranean, Crossbred and Jafarabadi, respectively. The duration of lactation was the most important source of variation over milk production, followed by the year of parturition, herd, parturition order, genetic group and month of parturition.

Genetic parameters estimate for milk and mozzarella cheese yield, fat and protein percentage in dairy buffaloes in Brazil

The aim of this study was analyze the (co)variance components and genetic and phenotypic relationships in the following traits: accumulated milk yield at 270 days (MY270,), observed until 305 days of lactation; accumulated milk yield at 270 days (MY270/A) and at 305 days (MY305), observed until 335 days of lactation; mozzarella cheese yield (MCY) and fat (FP) and protein (PP) percentage, observed until 335 days of lactation. The (co)variance components were estimated by Restricted Maximum Likelihood methodology in analyses single, two and three-traits using animal models. Heritability estimated for MY270, MY270/A, MY305, MCY, FP and PP were 0.22; 0.24, 0.25, 0.14, 0.29 and 0.40 respectively. The genetic correlations between MCY and the variables MY270, MY270/A, MY305, PP and FP was: 0.85; 1.00; 0.89; 0.14 and 0.06, respectively. This way, the selection for the production of milk in long period should increase MCY. However, in the search of animals that produce milk with quality, the genetic parameters suggest that another index should be composed allying these studied traits.

Correlation between acidic ninhydrin and HPLC methods to evaluate fraudulent addition of whey in milk

The acidic ninhydrin spectrophotometric method (ANSM) for quantitative determination of free and bound sialic acid of milk glycoprotein has been proved to be fast and efficient for routine detection of fraudulent addition of rennet whey to fluid milk. In this research the ANSM was compared with the high performance liquid chromatography (HPLC) method, internationally recommended for caseinomacropeptide (CMP) determination, which besides its high accuracy is more sophisticated and requires trained personnel. For several sample conditions (raw milk and milk with variable added amounts of rennet cheese whey), the methods showed an excellent linear correlation, with r = 0.981 when milk was deproteinized with a 120 g.L-1 final concentration of trichloroacetic acid (TCA) concentration. The best correlations could be seen with final concentrations of 100 g.L-1 and 80 g.L-1 TCA; respectively, r = 0.992 and 0.993.